Convert to a data.frame

Description

Equivalent to as_polars_df(x, …)$to_data_frame(…).

Usage

## S3 method for class 'RPolarsDataFrame'
as.data.frame(x, ..., int64_conversion = polars_options()\$int64_conversion)

# S3 method for class 'RPolarsLazyFrame'
as.data.frame(
  x,
  ...,
  n_rows = Inf,
  type_coercion = TRUE,
  predicate_pushdown = TRUE,
  projection_pushdown = TRUE,
  simplify_expression = TRUE,
  slice_pushdown = TRUE,
  comm_subplan_elim = TRUE,
  comm_subexpr_elim = TRUE,
  streaming = FALSE,
  no_optimization = FALSE,
  inherit_optimization = FALSE,
  collect_in_background = FALSE
)

Arguments

`x`	An object to convert to a data.frame.
`…`	Additional arguments passed to methods.
`int64_conversion`	How should Int64 values be handled when converting a polars object to R? `“double”` (default) converts the integer values to double. `“bit64”` uses `bit64::as.integer64()` to do the conversion (requires the package `bit64` to be attached). `“string”` converts Int64 values to character.
`n_rows`	Number of rows to fetch. Defaults to `Inf`, meaning all rows.
`type_coercion`	Logical. Coerce types such that operations succeed and run on minimal required memory.
`predicate_pushdown`	Logical. Applies filters as early as possible at scan level.
`projection_pushdown`	Logical. Select only the columns that are needed at the scan level.
`simplify_expression`	Logical. Various optimizations, such as constant folding and replacing expensive operations with faster alternatives.
`slice_pushdown`	Logical. Only load the required slice from the scan level. Don’t materialize sliced outputs (e.g. `join$head(10)`).
`comm_subplan_elim`	Logical. Will try to cache branching subplans that occur on self-joins or unions.
`comm_subexpr_elim`	Logical. Common subexpressions will be cached and reused.
`streaming`	Logical. Run parts of the query in a streaming fashion (this is in an alpha state).
`no_optimization`	Logical. Sets the following parameters to `FALSE`: `predicate_pushdown`, `projection_pushdown`, `slice_pushdown`, `comm_subplan_elim`, `comm_subexpr_elim`.
`inherit_optimization`	Logical. Use existing optimization settings regardless the settings specified in this function call.
`collect_in_background`	Logical. Detach this query from R session. Computation will start in background. Get a handle which later can be converted into the resulting DataFrame. Useful in interactive mode to not lock R session.

Conversion to R data types considerations

When converting Polars objects, such as DataFrames to R objects, for example via the as.data.frame() generic function, each type in the Polars object is converted to an R type. In some cases, an error may occur because the conversion is not appropriate. In particular, there is a high possibility of an error when converting a Datetime type without a time zone. A Datetime type without a time zone in Polars is converted to the POSIXct type in R, which takes into account the time zone in which the R session is running (which can be checked with the Sys.timezone() function). In this case, if ambiguous times are included, a conversion error will occur. In such cases, change the session time zone using Sys.setenv(TZ = "UTC") and then perform the conversion, or use the $dt$replace_time_zone() method on the Datetime type column to explicitly specify the time zone before conversion.

# Due to daylight savings, clocks were turned forward 1 hour on Sunday, March 8, 2020, 2:00:00 am
# so this particular date-time doesn't exist
non_existent_time = as_polars_series("2020-03-08 02:00:00")\$str\$strptime(pl\$Datetime(), "%F %T")

withr::with_envvar(
  new = c(TZ = "America/New_York"),
  {
    tryCatch(
      # This causes an error due to the time zone (the `TZ` env var is affected).
      as.vector(non_existent_time),
      error = function(e) e
    )
  }
)
#> <error: in to_r: ComputeError(ErrString("datetime '2020-03-08 02:00:00' is non-existent in time zone 'America/New_York'. You may be able to use `non_existent='null'` to return `null` in this case.")) When calling: devtools::document()>

withr::with_envvar(
  new = c(TZ = "America/New_York"),
  {
    # This is safe.
    as.vector(non_existent_time\$dt\$replace_time_zone("UTC"))
  }
)
#> [1] "2020-03-08 02:00:00 UTC"

Convert to a data.frame

Description

Usage

Arguments

Conversion to R data types considerations

See Also